Classification of EEG-based Brain Waves for Motor Imagery using Support Vector Machine

Riyadi, Munawar Agus; Prakoso, Teguh; Whaillan, Finade Oza; Wahono, Marcelinus David; Hidayatno, Achmad

doi:10.1109/icecos47637.2019.8984565

Cited by 10 publications

(7 citation statements)

References 10 publications

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“…The overall system shows good results. A similar study using muse headband sensor with FFT feature extraction method proved good results for five classes [9]. Compared to the study [12], which used the same extraction and classification methods, they provided an accuracy success rate of 86.7% motor imagery of only three classes.…”

Section: Resultsmentioning

confidence: 77%

“…The next stage is the windowing process with the hamming type. Hamming windowing process using ( 3) and ( 4) [9].…”

Section: 𝑁 = 𝑠𝑎𝑚𝑝𝑙𝑖𝑛𝑔 X 𝑓𝑠mentioning

confidence: 99%

“…Portable EEG was then used to overcome the problem. Riyadi et al [9] conducted a study on five classes of movement pattern recognition with a portable EEG sensor using beta and gamma waves. The number of electrodes used is four channels using a support vector machine (SVM).…”

Section: Introductionmentioning

confidence: 99%

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Classification of motor imagery brain wave for bionic hand movement using multilayer perceptron

Priyatno

Prakoso

Riyadi

2022

Sinergi

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Physical disability due to amputation can affect a person's quality of life due to limited movement in performing daily activities. Bionic hands are used to help someone with an amputation disability. This research developed a bionic hand control based on electroencephalography sensors capable of measuring the brain's bioelectric activity. The classified brain wave was then translated as activity pattern information. The alpha & beta waves were the focus of this work. This study demonstrated a method to extract and classify motor imagery of brainwave activity patterns. The Fast Fourier Transform (FFT) method extracts motor imagery characteristics. The extraction of features is then classified by the Multilayer Perceptron (MLP) method for five classes of bionic hand movement. Testing was conducted with two scenarios. The first test motor imagery without additional movement showed an accuracy of 77.20 %, while the second test motor imagery combined with head movement showed an accuracy of 84.40% for five classes. The system based on motor imagery has been implemented in a bionic hand that shows the applicability of the proposed method.

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Section: Resultsmentioning

confidence: 77%

“…The next stage is the windowing process with the hamming type. Hamming windowing process using ( 3) and ( 4) [9].…”

Section: 𝑁 = 𝑠𝑎𝑚𝑝𝑙𝑖𝑛𝑔 X 𝑓𝑠mentioning

confidence: 99%

See 1 more Smart Citation

Classification of motor imagery brain wave for bionic hand movement using multilayer perceptron

Priyatno

Prakoso

Riyadi

2022

Sinergi

Self Cite

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“…Considering that SVM classifier has been widely used in EEG classification [33][34][35], we use SVM classifier for classification in this paper. We divide the data set into two sets: training set and test set.…”

Section: Preprocessing and The Flow Chart Of The Experimentmentioning

confidence: 99%

EEG Signal Classification Using Manifold Learning and Matrix‐Variate Gaussian Model

Zhu

Yang

et al. 2021

Computational Intelligence and Neuroscience

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In brain-computer interface (BCI), feature extraction is the key to the accuracy of recognition. There is important local structural information in the EEG signals, which is effective for classification; and this locality of EEG features not only exists in the spatial channel position but also exists in the frequency domain. In order to retain sufficient spatial structure and frequency information, we use one-versus-rest filter bank common spatial patterns (OVR-FBCSP) to preprocess the data and extract preliminary features. On this basis, we conduct research and discussion on feature extraction methods. One-dimensional feature extraction methods like linear discriminant analysis (LDA) may destroy this kind of structural information. Traditional manifold learning methods or two-dimensional feature extraction methods cannot extract both types of information at the same time. We introduced the bilinear structure and matrix-variate Gaussian model into two-dimensional discriminant locality preserving projection (2DDLPP) algorithm and decompose EEG signals into spatial and spectral parts. Afterwards, the most discriminative features were selected through a weight calculation method. We tested the method on BCI competition data sets 2a, data sets IIIa, and data sets collected by our laboratory, and the results were expressed in terms of recognition accuracy. The cross-validation results were 75.69%, 70.46%, and 54.49%, respectively. The average recognition accuracy of new method is improved by 7.14%, 7.38%, 4.86%, and 3.8% compared to those of LDA, two-dimensional linear discriminant analysis (2DLDA), discriminant locality property projections (DLPP), and 2DDLPP, respectively. Therefore, we consider that the proposed method is effective for EEG classification.

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“…The distribution of handicap prevalence in Tunisia in 2013 [1] Eye motion is detected, and the motion tracking system begins to track the chair's movement through the webcam and ATmega1284P virtual serial communication. Steering control two-wheeled wheelchair designed has also been adopted, which has no caster and moves with only two wheels [10]. The gyro sensor measures the body's pitch angle, and the two in-wheel motors are used for moving the body.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Fabrication of Smart Robotic Wheelchair Instructed by Head Gesture

Mudassir

Mujahid

2021

IJEEI

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The confronting problem faced by the handicapped, paralyzed, disabled, and quadriplegic people is their independent mobility. They need external assistance to perform their daily life activities. This paper aims to solve that problem by smart designing and deployment of the robotic wheelchair for those who cannot perform their voluntary activities and movements. The proposed automated wheelchair comprises two parts; the first part is the user's helmet that works as a master device, and the second part is a slave device, a smart wheelchair. The master device consists of an accelerometer, microcontroller, and wireless transmitter, in which the Accelerometer recognizes the movements of the user's head and transmits the signal according to the tiltation of the user's head. Besides this, the slave device consists of a wireless receiver, microcontroller, Gyroscope, power MOSFETs, and DC geared motors mounted on a smart wheelchair, which response as per the instructions of the master device. Furthermore, the paper also provides a brief construction of this mechatronic and amphibian system using static and dynamic equations.

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Classification of EEG-based Brain Waves for Motor Imagery using Support Vector Machine

Cited by 10 publications

References 10 publications

Classification of motor imagery brain wave for bionic hand movement using multilayer perceptron

Classification of motor imagery brain wave for bionic hand movement using multilayer perceptron

EEG Signal Classification Using Manifold Learning and Matrix‐Variate Gaussian Model

Modeling and Fabrication of Smart Robotic Wheelchair Instructed by Head Gesture

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